Svc compensation strategy optimization method

ABSTRACT

An SVC compensation strategy optimization method, comprising: calculating a weak voltage node in a fault state based on risk measure; calculating the weak voltage node in a normal state based on a static stability margin; and determining an optimal SVC distribution point and calculating the optimal configuration of SVC capacity. The SVC compensation strategy optimization method overcomes the defects in the prior art, such as low reliability, low optimization precision, poor applicability, etc., and has the advantages of high reliability, high optimization precision, and good applicability.

TECHNICAL FIELD

The present invention relates to the Var compensation technical field,specifically to a Static Var Compensator (SVC) Compensation StrategyOptimization Method.

BACKGROUND

With the rapid development of the power grid in China, said grid willbecome a super-large synchronous/asynchronous mixed interconnected powergrid with the highest voltage grade, the maximum long distance powertransmission capacity and the widest interconnected power grid coveragearea in the near future. However, the power grid interconnection alsoinevitably brings some problems while bringing great benefit. The systemstructure and its running way are getting more and more complex andvariable, easily leading to the chain reaction of accidents which willcause widespread blackout. This is proved by the successive majorblackout accidents in several large power grids around the world inrecent years.

With the increase of power use intensity in large cities and loadcenters and the application of super-high voltage long distancetransmission lines, the stability problem of power system is gettingmore and more prominent. Besides, with the development of industrialtechnology, the impact loads of the industrial electric arc furnace,electric locomotive, steel rolling machine and large semiconductor ACequipment are increasing, the reactive power of these loads changesviolently and may destabilize the system voltage. Therefore, improvingthe stability of interconnected power grid and inhibiting the voltagefluctuation have been becoming a hot spot of concern for people.

In order to improve the voltage stability of power grid, enhance thetransmission capacity, reduce the grid loss, inhibit the low-frequencyoscillation among areas, and meet the safe and reliable runningrequirements of electric system and the commercial running requirementsof power market, it is urgently required to improve the controllabilityand adjustability of the system parameter. The researchers have beenalways searching more advanced and effective control measures. It haslong been considered of changing the topology structure and parameter ofnetwork to adjust the line trend and of manufacturing some equipmentsuch as fixed series or parallel compensation device to control thesystem trend. However, most of these devices are based on mechanicalswitches, the mechanical inertia limits the improvement of its runningspeed, its mechanical action has poor reliability and short servicelife, and cannot meet the demand of modern electric system trendadjustment and the demand for controlling in other aspects. Seeking newmeasure that can continuously, quickly and accurately control the systemtrend is always the objective.

With the development of high-power power electronic technology and thematurity of computer control technology, the Flexible AC TransmissionSystem (FACTS) technology emerges as the times require. As one of theFACTS devices, the SVC is a static var compensator based on powerelectronic technology, and it can continuously and dynamically adjustthe bus voltage of system, alleviate the impact of power systemdisturbance to bus voltage, and keep the bus voltage of power systemwithin a normal scope. Different from traditional parallel capacitor andreactor, the SVC has the advantages of high response speed, smoothadjustment and dynamic tracking bus reactive power; and the SVC can beconsidered as the reactive power supply in the power system besides thegenerator and can also be considered as a pure reactive load. From theview of power grid structure, the SVC is a partial structure controldevice, which adjusts the dynamic structure of power grid at certainextent and guarantees the basic dynamic property of the power system.From the view of power system trend distribution, the SVC is a feedbackcompensation measure, the influence thereof on power system can beconsidered as the topology change to related parameter space toguarantee the partial topology equivalence of power system. In thissense, the selection of installation place and the optimization ofinstallation capacity of the SVC are especially important.

The compensation strategy optimization technology of SVC reactivecompensation device includes two sides: SVC optimized compensation spotand optimized capacity configuration.

As to the determination and selection of weak line and bus of powergrid, i.e., reactive compensation device SVC compensation point, theprior art adopts the method of calculating the static load margin whichrepresents the voltage stability of the system. The static load marginindicates the ratio of the difference between the total apparent powerof load in critical running state and the total apparent power of loadin normal state to the total apparent power in normal state, as shown informula (1):

$\begin{matrix}{\lambda = \frac{S^{L} - S^{N}}{S^{N}}} & (1)\end{matrix}$

In formula (1), λ indicates the static load margin; S^(L) indicates thetotal apparent power in critical running state; and S^(N) indicates thetotal apparent power in normal state.

The transition way of the power system from normal running state tocritical state includes the ways of increasing the load at single-loadnode, increasing the load at multiple load nodes and increasing the loadin the whole grid. Different load increasing way may obtain differentstatic load margin. After determining the load increasing way, thecritical point is uniquely determined. The prior art generally adoptsthe way of increasing load at single-load node to calculate the staticload margin of each node, then rank the nodes, and determine the severalnodes with minimal load margin as the voltage weak point, i.e., the mostdeserved compensable points of SVC compensation device.

When calculating the SVC optimized capacity configuration, the currentmethod adopts multiple objective optimization algorithm, and the targetfunction is shown as formula (2):

minf=I _(svc) +L _(grid)  (2)

In formula (2), I_(svc) indicates the total invested maintenance cost ofSVC; and L_(grid) indicates the grid loss of the system.

After adding the SVC at the compensation point, the correspondinginvested maintenance cost will be caused according to the reactivecapacity of SVC, and the system structure and trend will change so as tochange the system grid loss. Therefore, it is desired to obtain the bestcapacity configuration of configuration point by the optimizationcalculation of the above formula.

The current SVC compensation strategy optimization method has followingdrawbacks:

(1) As to the determination of system weak line and bus, i.e., theselection and optimization technology of SVC compensation point, thecurrent method only considers the stability in normal running state, butnot technically analyzes the system stability and corresponding weaklink in fault state. In the system chain fault state, the physical linkand mathematic relation among all elements of the system are not clear,and this will prevent the original optimization technology fromperforming accurate and effective reactive compensation alleviation andvoltage enhance function in the system chain fault state, evenaccelerate the system collapse.

(2) As to the capacity optimization configuration of SVC compensationdevice, in the multiple objective optimization target function used inprior art, two variables have different scales and quantities,therefore, in the multiple objective optimization process, shieldphenomenon may occur, causing inaccurate optimization result andunavailable actual optimization strategy.

In view of the above, in the process of realizing the present invention,the inventor have found that the prior art at least has thedisadvantages of low reliability, low optimization precision and poorapplicability.

SUMMARY OF THE INVENTION

The present invention aims to provide a SVC Compensation StrategyOptimization Method according to the above mentioned problems in orderto realize the advantages of high reliability, high optimizationprecision and good applicability.

In order to realize the above mentioned objectives, the presentinvention adopts the following technical solution: a SVC CompensationStrategy Optimization Method mainly comprises:

a. Calculating a weak voltage node in a fault state based on riskmeasurement;

b. Calculating the weak voltage node in a normal state based on a staticstability margin; and

c. Determining an optimal SVC distribution point and calculating theoptimal configuration of SVC capacity.

Further, the step a specifically comprises:

a1. Credibility measurement: measuring the uncertainty of the power gridcatastrophic accident by the credibility measurement and establishingthe evaluation model of the catastrophic accident according to thereliability theory;

a2. Global fuzzy safety measurement: the ability of the element to bearthe disturbance varies in certain region [D_(low), D_(up)]; when thedisturbance is greater than D_(up), the element is unsafe; when thedisturbance is less than D_(low), the element is normal; when thedisturbance occurs within said region, the element running state isuncertain and can be drawn by the region number; and the region numberis a type of special fuzzy number, and the membership degree functioncan be used to draw the change trend;

a3. Risk measurement: the risk measurement M_(risk) is i a comprehensivemeasurement to M_(cr) and M_(GFS) and is positively related to theM_(cr) M_(GFS), it can be drawn by the Larsen operator, and themathematical expression is:

M _(risk) =M _(cr) M _(GFS)  (14);

a4. SVC node distribution model algorithm based on risk measurement: onthe basis of catastrophic accident risk evaluation method, analyzing therunning risk of the power grid, forecasting the weak branch in accidentprocess, obtaining the sequence of possible catastrophic accidents andthe sequence of chain faults of the power grid, and providing basis forSVC compensation point.

Further, in the step a1, the credibility measurement M_(cr)(A) ofoccurrence of catastrophic accident A is:

$\begin{matrix}{{{{M_{cr}(A)} = {\frac{1}{2}\left( {{M_{pos}(A)} + {M_{nec}(A)}} \right)}};}{{Wherein}\text{:}}} & (3) \\{{{M_{nec}(A)} = {1 - {M_{pos}\left( \overset{\_}{A} \right)}}};} & (4)\end{matrix}$

In formula (3) and formula (4), Ā is the complementary set of A; andM_(nec)(A) indicates the impossibility degree of Ā;

According to formula (3) and formula (4), the value in the credibilitymeasurement varies within [0,1]; when the value is 1, the accident A isinevitable; when the value is 0, the accident A is impossible; and whenthe value is between 0 and 1, the credibility of occurrence of theaccident A increases with the increase of measurement.

Further, in the step a2, the over limit degree of the power systemcomponent is used to represent the chain fault severity, and 5 severitymembership degrees δt(t=1, 2, . . . , 5) are used to respectivelydescribe the severity of branch overload, load miss, bus voltage, activeand reactive output of a generator.

Further, in the step a4, the N−1 accident is considered as the initialaccident, ranking the risk measurements of all accident transmissionstages, and the most dangerous accident in one stage is considered asthe initial accident of the next stage; when the accident causes the nonconvergence of power grid trend or more than 20% of load loss, it is acatastrophic accident; and N is a natural number.

Further, the step b specifically comprises:

Obtaining the load margin of the system or node by the nonlinearplanning method, and in the condition of meeting all limits of system,determining the maximum value of load increase in the power system, andthe mathematical model thereof is:

min−λ  (15);

The limiting condition (s.t.) of formula (15) is as follows:

${P_{gi} - P_{Li} - {V_{i}{\sum\limits_{j \in i}{V_{j}\left( {{G_{ij}\cos \; \theta_{ij}} + {B_{ij}\sin \; \theta_{ij}}} \right)}}} - {\lambda \; b_{p\; i}}} = 0$${Q_{gi} - Q_{Li} - {V_{i}{\sum\limits_{j \in i}{V_{j}\left( {{G_{ij}\sin \; \theta_{ij}} - {B_{ij}\cos \; \theta_{ij}}} \right)}}} - {\lambda \; b_{qi}}} = 0$Pg_(i m i n) ≤ Pg_(i) ≤ Pg_(ima x)(i = 1, 2, …  , n_(G))Qg_(im i n) ≤ Qg_(i) ≤ Qg_(i ma x)V_(i m i n) ≤ V_(i) ≤ V_(i ma x)(i = 1, 2, …  , n)P_(li m i n) ≤ P_(li) ≤ P_(lima x)(i = 1, 2  …  , n_(l))

In formula (15) and the limiting conditions thereof: n indicates thetotal number of nodes; P_(gi) and Q_(gi) respectively indicate theactive and reactive power of the node i, P_(Li) and Q_(Li) respectivelyindicate the active and reactive load power of node i; V_(i) and θ_(i)respectively indicates the voltage amplitude and phase angle of the nodei; the node admittance matrix element is G_(ij)+B_(ij); b_(pi) andb_(qi) respectively indicate the load increase directions.

In formula (15) and the limiting conditions thereof: n_(l) indicates theamount of branches, Pg_(imin) and Pg_(imax) respectively indicate theupper and lower limits of active treatment of the generator i; Qg_(imin)and Qg_(imax) respectively indicates the upper and lower limits ofreactive actions of the generator i; V_(imin) and V_(imax) respectivelyindicates the upper and lower limits of voltage of the node i; P_(limin)and P_(limax) indicate the upper and lower limits for the branch i totransmit the active power.

Further, the step c specifically includes:

c1. The multiple objective SVC capacity configuration optimizationmodel;

c2. The fuzzy treatment of target function by using the fuzzy set theorymethod; and

c3. The fuzzy single objective optimization model.

Further, the step c1 specifically comprises:

In the process of configuring the SVC device to the power grid, it isrequired to consider both the increase of the system voltage stabilityand the cost of installing the SVC after installing the SVC. Therefore,when establishing the optimization model, the target function shouldinclude the change of voltage stability and the paid cost.

The target function:

Consider the target function of the static load margin:

F₁=maxλ  (16);

Consider the target function of the investment fee:

$\begin{matrix}{{F_{2} = {{\min {\sum\limits_{i \in \Omega}a_{i}}} + {b_{i}y_{i}}}};} & (17)\end{matrix}$

wherein: λ indicates the static load margin of the system; Ω indicatesthe selected reactive compensation node, y_(i) indicates thecompensation reactive capacity of the compensation node i, and a_(i) andb_(i) respectively indicate the relationship parameters between thecompensation price and the compensation capacity.

Limiting conditions:

${P_{gi} - P_{Li} - {V_{i}{\sum\limits_{j \in i}{V_{j}\left( {{G_{ij}\cos \; \theta_{ij}} + {B_{ij}\sin \; \theta_{ij}}} \right)}}} - {\lambda \; b_{{pi}\;}}} = 0$${Q_{gi} + Q_{ci} - Q_{Li} - {V_{i}{\sum\limits_{j \in i}{V_{j}\left( {{G_{ij}\sin \; \theta_{ij}} - {B_{{ij}\;}\cos \; \theta_{ij}}} \right)}}} - {\lambda \; b_{qi}}} = 0$Pg_(im i n) ≤ Pg_(i) ≤ Pg_(ima x)Qg_(im i n) ≤ Qg_(i) ≤ Qg_(i ma x)V_(im i n) ≤ V_(i) ≤ V_(ima x)P_(li m i n) ≤ P_(li) ≤ P_(lima x)Q_(cim i n) ≤ Q_(ci) ≤ Q_(cima x)

wherein, P_(gi) and Q_(gi) respectively indicate the active and reactivepower of the node i, P_(Li) and Q_(Li) respectively indicate the activeand reactive load power of node i; Q_(ci) indicates the compensationcapacity of the compensation node i; V_(i) and θ_(i) respectivelyindicates the voltage amplitude and phase angle of the node i I; thenode admittance matrix element is G_(ij)+B_(ij); b_(pi) and b_(qi)respectively indicate the load increase directions.

Pg_(imin) and Pg_(imax) respectively indicate the upper and lower limitsof active treatment of the generator i; Qg_(imin) and Qg_(imax)respectively indicates the upper and lower limits of reactive actions ofthe generator i; V_(imin) and V_(imax) respectively indicates the upperand lower limits of voltage of the node i; P_(limin) and P_(hd limax)indicate the upper and lower limits for the branch i to transmit theactive power; and Q_(cimin) and Q_(cimax) respectively indicate themaximum value and minimal value of compensation capacity of thecompensation node i.

Further, the step c2 specifically comprises:

1) The greater the static load margin, the better the voltage stabilityof system, so the target function F₁ belongs to the maximum targetfunction, and the membership degree function μ(F₁) is selected as thelinear monotonic increasing function:

$\begin{matrix}{{\mu \left( F_{1} \right)} = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu} F_{1}} \leq F_{1m\; i\; n}} \\\frac{F_{1} - F_{1m\; i\; n}}{F_{1m\; {ax}} - F_{1m\; i\; n}} & {{{if}\mspace{14mu} F_{1m\; i\; n}} \leq F_{1} \leq F_{1m\; a\; x}} \\1 & {{{if}\mspace{14mu} F_{1}} \geq F_{1{ma}\; x}}\end{matrix} \right.} & (18)\end{matrix}$

wherein, F_(1min) indicates the unacceptable target value; F_(1max)indicates the ideal target value.

2) The less the investment cost, the better the target function F₂, sothe target function F₂ belongs to the minimal target function, and themembership degree function μ(F₂) is selected as the linear monotonicdecreasing function:

$\begin{matrix}{{\mu \left( F_{2} \right)} = \left\{ {\begin{matrix}0 & {{{if}\mspace{14mu} F_{2}} \leq F_{2m\; i\; n}} \\\frac{F_{2m\; {ax}} - F_{2}}{F_{2m\; {ax}} - F_{2m\; i\; n}} & {{{if}\mspace{14mu} F_{2m\; i\; n}} \leq F_{2} \leq F_{2m\; a\; x}} \\1 & {{{if}\mspace{14mu} F_{2}} \geq F_{2m\; i\; n}}\end{matrix};} \right.} & (19)\end{matrix}$

wherein, F_(2max) indicates the unacceptable target value; F_(2min)indicates the ideal target value. The diagram of linear monotonicincreases or decreases membership function.

Further, the step c3 specifically comprises:

The decider applies different weights to all fuzzy target functions andconverts the multiple objective functions into the fuzzy singleobjective function, and the optimization model of SVC capacityconfiguration can be expressed as:

$\begin{matrix}{{F = {\max \left( {\sum\limits_{i = 1}^{2}{\omega_{i}{\mu \left( F_{i} \right)}}} \right)}};} & (20)\end{matrix}$

The limiting condition is the same as the limiting condition of themultiple objective optimization model established in formula (16) andformula (17).

The SVC Compensation Strategy Optimization Method in all embodiments ofthe present invention mainly comprises: calculating a weak voltage nodein a fault state based on risk measurement; calculating the weak voltagenode in a normal state based on a static stability margin; anddetermining an optimal SVC distribution point and calculating theoptimal configuration of SVC capacity. Therefore, the risk measurementanalysis technology can be combined with the original static load marginanalysis method to analyze the reactive weak points of the whole systemin the normal state and the fault state and provide the optimizationsolution of optimal SVC access point, thereby overcoming thedisadvantages of the prior art of low reliability, low optimizationprecision and poor applicability and realizing the advantages of highreliability, high optimization precision and good applicability.

Other features and advantages of the present invention will be describedin the following description, and partially be obvious in thedescription or known by implementing the present invention.

The technical solution of the present invention will be furtherdescribed in detail below by way of drawings and embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawing is used to provide further understanding to the presentinvention, form one part of the description, and explain the inventiontogether with the embodiments of the invention without limiting thepresent invention. In the drawings:

FIG. 1 is a schematic diagram of severity membership degree distributionrule;

FIG. 2 is a risk measurement evaluation flow chart of power grid chainaccident;

FIG. 3 is a multiple objective conversion fuzzy membership function;

FIG. 4 is an implementation flow chart of SVC compensation strategyoptimization method;

FIG. 5 is a simplified diagram of electric wiring of technicalverification test system;

FIGS. 6(a)-(b) are the PV curve comparison of node 11 in Gansu Guazhoubefore and after the compensation; and

FIGS. 7(a)-(b) are the PV curve comparison of node 31 in Gansu Yumenbefore and after the compensation.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The preferred embodiment of the present invention will be describedbelow in combination with the drawings, and it should be understood thatthe preferred embodiment described here is only used to describe andexplain the present invention without limiting the present invention.

When solving the access point optimization problem of the SVCcompensation, the prior art cannot accurately handle the weak links insystem fault condition and therefore cannot accurately find the optimalaccess point. The present invention adopts the risk measurement analysistechnology of using the risk measure of line chain accident in system tofind the weak link in system fault state and combining the originaloptimization technology to find the optimal access point of SVC innormal and fault states of the system.

When optimizing the SVC capacity configuration in prior art, thevariables in the multiple objective optimization target function havedifferent dimensions, leading to the problem of inaccurate optimizationresult. The present invention adopts the fuzzy technology to fuzzify thetarget function by using the membership degree function, to convert thetarget function with dimension to the target function without dimensionto provide it with comparability, and to provide each target functionwith different weights, thus converting multiple objective problems intosingle objective problem.

According to the embodiments of the present invention, as shown fromFIG. 1 to FIG. 7, a SVC Compensation Strategy Optimization Method mainlycomprises the following steps:

1. According to the reactive power in-situ balance principle, theoptimal SVC access points should be located on two sides of the weakestbranch. The power grid accident is combined with the safety, and therisk theory is used to identify the weak branch in power grid. The modeladopts the N−1 accident as the initial accident, ranking the riskmeasurement of N−k accidents and identifying the sequence of possibleaccidents in power grid. According to the frequency of power grid branchin accident sequence and the influence degree thereof on the sequence,the weak branch of West Huanghe River power grid and the considered weaknode are obtained.

2. The static load margin indicates the distance from the currentrunning state to the system collapse, the less the static load margin,the worse the voltage stability, and the easier the voltage collapseafter system disturbance. By calculating the static load margin of allnodes, the node with minimal static load margin is used as the SVCcompensation node to effectively prevent the voltage collapse andguarantee safe and stable running of system.

3. Comprehensively considering the item 1 and the item 2 and determiningthe optimal distribution point of SVC compensation.

4. Meanwhile, simultaneously considering the system static load marginand the SVC device installation investment fees, establishing a multipleobjective optimization model, and fuzzifying the target function toobtain the fuzzy single objective optimization model, and using theprimal—dual interior point method to obtain the optimal compensationcapacity of each compensation node.

5. Using the PSD-BPA power system analysis software to model the WestHuanghe River power grid in Gansu, analyzing the safety and stability ofthe power grid before and after installing the SVC according to thetechnology in the present invention, and researching the risk of powergrid before and after installing the SVC in system N−k accident in acomputer of Intel(R) Core(TM) i3 CPU, 3.20 GHz, 2G and 32-bit operationsystem.

Specifically, referring to FIG. 1-FIG. 7, the complete technicalsolution of implementing the SVC Compensation Strategy OptimizationMethod in the above mentioned embodiment is as follows:

1. Fault Risk Evaluation Measurement System

(1) Credibility Measurement

In view of the above, the possibility measurement only subjectivelydescribes the easiness of accident, actually, the accident with thepossibility of 1 may not necessarily happen, i.e., the possibilitymeasurement does not have the self-duality. In order to make up thisdefect, this embodiment adopts the credibility measurement to measurethe uncertainty of catastrophic accident of the power grid, andestablish the evaluation model of catastrophic accident according to thecredibility theory.

The credibility measurement M_(cr)(A) of the catastrophic accident A is:

$\begin{matrix}{{{M_{c\; r}(A)} = {\frac{1}{2}\left( {{M_{pos}(A)} + {M_{nec}(A)}} \right)}}{{wherein},}} & (3) \\{{M_{nec}(A)} = {1 - {M_{pos}\left( \overset{\_}{A} \right)}}} & (4)\end{matrix}$

In formula (3) and formula (4), Ā is the complementary set of A; andM_(nec)(A) indicates the impossibility degree of Ā;

According to formula (3) and formula (4), the value in the credibilitymeasurement varies within [0,1]; when the value is 1, the accident A isinevitable; when the value is 0, the accident A is impossible; and whenthe value is between 0 and 1, the credibility of occurrence of theaccident A is increased as the increase of measurement.

Taking M_(pos)(A_(j)) and M_(pos)(Ā_(j)) as examples, when the accidentis transmitted to the j stage, if the current I_(ij) of the branchL_(ij)(i=1, 2, . . . , n_(j)) is a fuzzy variable, the correspondingmembership function is μ_(ij)(I_(ij)). The possibility of multiplehidden failures is far less than the possibility of single hiddenfailure, so the influence of multiple hidden failure can be ignored, andit is considered that the set B_(j) composed of the fault elementstransmitted by the accident to all stages only has 1 branch L_(mj) thatis cut off because of the hidden failure, and the current on the L_(if)before the cutting is Ī_(ij). According to the definition of jointreliability distribution function:

$\begin{matrix}{{M_{pos}\left( A_{j} \right)} = {{\sup\left\lbrack {\bigwedge\limits_{\underset{i \neq m}{i = {1\sim n_{j}}}}{{\mu_{ij}\left( {I_{ij} \geq {\overset{\_}{I}}_{ij}} \right)}\bigwedge{\mu_{mj}\left( {I_{mj} \leq {\overset{\_}{I}}_{mj}} \right)}}} \right\rbrack} = {{1\bigwedge 1\bigwedge\ldots\bigwedge 1\bigwedge{\mu_{mj}\left( {\overset{\_}{I}}_{mj} \right)}} = {\mu_{mj}\left( {\overset{\_}{I}}_{mj} \right)}}}} & (5) \\{{M_{pos}\left( {\overset{\_}{A}}_{j} \right)} = {{\sup\left\lbrack {\bigwedge\limits_{\underset{i \neq m}{i = {1\sim n_{j}}}}{{\mu_{ij}\left( {I_{ij} \leq {\overset{\_}{I}}_{ij}} \right)}\bigwedge{\mu_{mj}\left( {I_{mj} \geq {\overset{\_}{I}}_{mj}} \right)}}} \right\rbrack} = {{{\mu_{1j}\left( {\overset{\_}{I}}_{1j} \right)}\bigwedge{\mu_{2j}\left( {\overset{\_}{I}}_{2j} \right)}\bigwedge\ldots\bigwedge{\mu_{n_{j}j}\left( {\overset{\_}{I}}_{n_{j}j} \right)}\bigwedge 1} = {\bigwedge\limits_{\underset{i \neq m}{i = {1\sim n_{j}}}}{\mu_{ij}\left( {\overset{\_}{I}}_{ij} \right)}}}}} & (6)\end{matrix}$

According to formula (5) and formula (6):

M _(pos)(A)=M _(pos)(A₁)

M _(pos)(A₂)

M _(pos)(A _(k))  (7)

M _(nec)(A)=1−M _(pos)(Ā ₁)

M _(pos)(Ā ₂)

. . .

M _(pos)(Ā _(k))  (8)

(2) Global Fuzzy Safety Measurement

The severity of accident is drawn with the over-limit degree of elementssuch as branch, bus and generator. The traditional method obtains theglobal severity measurement M_(GS) of power grid by the weighted mean ofelement severity, this way ignoring the uncertainty of the elementdisturbance bearing capacity. In actual condition, the elementdisturbance bearing capacity always changes in a certain region[D_(low), D_(up)]. When the disturbance is greater than D_(up), theelement is unsafe; when the disturbance is less than D_(low), theelement is normal; when the disturbance is within this region, theelement running state is uncertain and can be drawn with the regionnumber; and the region number is a type of special fuzzy number, and themembership degree function can be used to draw the change trend.

In the embodiment of the invention, 5 severity membership degreesδt(t=1, 2, . . . , 5) are used to describe the severity of branchoverload, load miss, bus voltage, active and reactive output of agenerator. δ1, δ2 and δ3-δ5 respectively represent the large, small andmedium trapezoid distribution rule, referring to FIG. 1. Wherein, Sindicates the current state parameter of the element, and the trapezoiddistribution parameters S1 and S2 as well as Slim1 and Slim2respectively indicate the thresholds for element safe running and foraccident occurrence. All distribution parameters are standardized, andthe set values are shown in Table 1.

TABLE 1 Parameter setting of trapezoid distribution Severity membershipDistribution degree rule Parameter δ1 Large S1 = 1.10 pu, Slim1 = 1.30pu δ2 Small Slim2 = 0.80 pu, S2 = 0.95 pu δ3 Medium Slim2 = 0.90 pu, S2= 0.95 pu, S1 = 1.05 pu, Slim1 = 1.10 pu δ4 Medium Slim2 = 0 pu, S2 =0.90 pu, S1 = 1.07 pu, Slim1 = 1.15 pu δ5 Medium Slim2 = −0.02 pu, S2 =0.90 pu, S1 = 1.07 pu, Slim1 = 1.15 pu

The severity of chain accidents is represented by the over-limit degreeof power system components, and 5 severity membership degrees δt(t=1, 2,. . . , 5) are used to describe the severity of branch overload, loadmiss, bus voltage, active and reactive output of a generator.Specifically:

1) among the line overload severity, the line temperature over-limitexpresses the line overload, and the expression is shown as formula (9):

$\begin{matrix}{{{Sev}(S)} = \left\{ \begin{matrix}0 & {S < S_{1}} \\\frac{S - S_{1}}{S_{{li}\; m\; 1} - S_{1}} & {S_{1} < S < S_{l\; {im}\; 1}} \\1 & {S > S_{{li}\; m\; 1}}\end{matrix} \right.} & (9)\end{matrix}$

wherein, Sev(S) indicates the severity of line overload risk; Sindicates the current trend of line, and S₁

S_(lim1) respectively indicate the warning trend value and highest trendvalue of the line.

2) Load loss severity calculation formula, as shown in formula (10).

$\begin{matrix}{{{Sev}(L)} = \left\{ \begin{matrix}0 & {{\Delta \; L} < {\Delta \; L_{1}}} \\\frac{{\Delta \; L} - {\Delta \; L_{1}}}{{\Delta \; L_{{li}\; m\; 1}} - {\Delta \; L_{1}}} & {{\Delta \; L_{1}} < {\Delta \; L} < {\Delta \; L_{{li}\; m\; 1}}} \\1 & {{\Delta \; L} > {\Delta \; L_{l\; {im}\; 1}}}\end{matrix} \right.} & (10)\end{matrix}$

wherein, Sev(L) indicates the load loss severity; ΔL indicates theactual load loss; and ΔL₁ and ΔL_(lim1) respectively indicate the loadloss warning value and loss highest value.

3) Calculation formula of node state amount over-limit severity is shownin formula (11):

$\begin{matrix}{{{Sev}(X)} = \left\{ \begin{matrix}1 & {{X < X_{{li}\; m\; 2}},{X > X_{{li}\; m\; 1}}} \\\frac{X - X_{2}}{X_{{li}\; m\; 2} - X_{2}} & {X_{{li}\; m\; 2} < X < X_{2}} \\0 & {X_{2} < X < X_{1}} \\\frac{X - X_{1}}{X_{{li}\; m\; 1} - X_{1}} & {X_{1} < X < X_{{li}\; m\; 1}}\end{matrix} \right.} & (11)\end{matrix}$

wherein, Sev(X) indicates the node state amount over-limit severity, Xmay be the voltage U, active P or reactive Q; and X₁, X₂, X_(lim1) andX_(lim2) indicate the state amount over-limit calculation threshold ofall nodes.

The corresponding comprehensive severity membership degree δ_(t) ^(s)can be obtained from the membership degree δ_(t) of the element faultseverity:

$\begin{matrix}{\delta_{t}^{s} = {\sum\limits_{l = 1}^{r}{\delta_{t}(l)}}} & (12)\end{matrix}$

In formula (12), l indicates the component l(l=1, 2, . . . , r) of δtcorresponding element.

The global fuzzy safety measurement of power grid M_(GFS) is:

$\begin{matrix}{M_{GFS} = {\prod\limits_{t = 1}^{5}\delta_{t}^{s}}} & (13)\end{matrix}$

M_(GFS) comprehensively considers the influence of branch, bus andgenerator and reflects the influence degree of the disturbance on thepower grid. The less the M_(GFS) value, the better the safety of powergrid; and the greater the M_(GFS) value, the worse the safety of powergrid.

In the coefficient selection process, the coefficient of voltage U inthe node state amount to increase the influence of system voltageinstability and evaluate the global voltage safety of system.

(3) Risk Measurement

The catastrophic accident of power grid has multiple uncertainties, sothe risk measurement is generally used for evaluation.

The risk measurement M_(risk) is a comprehensive measurement to M_(cr)and M_(GFS) and is positively related to the M_(cr) and M_(GFS), it canbe drawn by the Larsen operator, and the mathematical expression is:

M _(risk) =M _(cr) M _(GFS)  (14)

(4) SVC Node Distribution Model Algorithm Based on Risk Measurement

The research finds that most catastrophic accidents of power grid causethe large-scale spread for unstable voltage, and the SVC can quicklyprovide the system the reactive support in the accident process andimprove the bus voltage. Therefore, the present application can, on thebasis of catastrophic accident risk evaluation method, analyze therunning risk of the power grid, forecast the weak branch in accidentprocess, obtain the sequence of possible catastrophic accidents and thesequence of chain faults of the power grid, and provide basis for SVCcompensation point.

The present invention can take the N−1 accident as the initial accident,ranking the risk measurements of all accident transmission stages, andthe most dangerous accident in one stage is considered as the initialaccident of the next stage; when the accident causes the non convergenceof power grid trend or more than 20% of load loss, it is thecatastrophic accident; and the uncertainty risk evaluation flow is shownin FIG. 2.

2. Static Load Margin

The load margin of a system or load can be obtained by the nonlinearplanning method, and in the condition of meeting all limits of thesystem, the object is how to determine the maximum value of loadincrease in the power system, and the mathematical model is:

min−λ  (15);

The limiting condition (s.t.) of formula (15) is as follows:

${P_{gi} - P_{Li} - {V_{i}{\sum\limits_{j \in i}{V_{j}\left( {{G_{ij}\cos \; \theta_{ij}} + {B_{ij}\sin \; \theta_{ij}}} \right)}}} - {\lambda \; b_{pi}}} = 0$${Q_{gi} - Q_{Li} - {V_{i}{\sum\limits_{j \in i}{V_{j}\left( {{G_{ij}\sin \; \theta_{ij}} - {B_{ij}\cos \; \theta_{ij}}} \right)}}} - {\lambda \; b_{qi}}} = 0$Pg_(im i n) ≤ Pg_(i) ≤ Pg_(i ma x)(i = 1, 2, …  , n_(G))Qg_(im i n) ≤ Qg_(i) ≤ Qg_(ima x)V_(imin) ≤ V_(i) ≤ V_(imax)(i = 1, 2  …  , n)P_(limi n) ≤ P_(li) ≤ P_(lima x)(i = 1, 2  …  , n_(l))

In formula (15) and the limiting conditions thereof: n indicates thetotal number of nodes; P_(gi) and Q_(gi) respectively indicate theactive and reactive power of the node i, P_(Li) and Q_(Li) respectivelyindicate the active and reactive load power of node i; V_(i) and θ_(i)respectively indicates the voltage amplitude and phase angle of the nodei; the node admittance matrix element is G_(ij)+B_(ij); b_(pi) andb_(qi) respectively indicate the load increase directions.

In formula (15) and the limiting conditions thereof: n_(l) indicates theamount of branches, Pg_(imin) and Pg_(imax) respectively indicate theupper and lower limits of active treatment of the generator i; Qg_(imin)and Qg_(imax) respectively indicates the upper and lower limits ofreactive actions of the generator i; V_(imin) and V_(imax) respectivelyindicates the upper and lower limits of voltage of the node i; P_(limin)and P_(limax) indicate the upper and lower limits for the branch i totransmit the active power.

3. SVC Capacity Optimization Configuration Algorithm

(1) Optimization model of multiple objective SVC capacity configuration

In the process of configuring the SVC device to the power grid, it isrequired to consider both the increase of the system voltage stabilityand the cost of installing the SVC after installing the SVC, therefore,when establishing the optimization model, the target function shouldinclude the change of voltage stability and the fee paid;

The target function;

Considering the target function of the static load margin:

F₁=max λ,  (16);

Considering the target function of the investment fee:

$\begin{matrix}{{F_{2} = {{\min {\sum\limits_{i \in \Omega}a_{i}}} + {b_{i}y_{i}}}};} & (17)\end{matrix}$

wherein: λ indicates the static load margin of the system; Ω indicatesthe selected reactive compensation node, y_(i) indicates thecompensation reactive capacity of the compensation node i, and a_(i) andb_(i) respectively indicate the relationship parameters between thecompensation price and the compensation capacity.

Limiting Conditions:

${P_{gi} - P_{Li} - {V_{i}{\sum\limits_{j \in i}{V_{j}\left( {{G_{ij}\cos \; \theta_{ij}} + {B_{ij}\sin \; \theta_{ij}}} \right)}}} - {\lambda \; b_{pi}}} = 0$${Q_{gi} + Q_{ci} - Q_{Li} - {V_{i}{\sum\limits_{j \in i}{V_{j}\left( {{G_{ij}\sin \; \theta_{ij}} - {B_{ij}\cos \; \theta_{ij}}} \right)}}} - {\lambda \; b_{qi}}} = 0$Pg_(imin) ≤ Pg_(i) ≤ Pg_(imax) Qg_(imin) ≤ Qg_(i) ≤ Qg_(imax)V_(imin) ≤ V_(i) ≤ V_(ima x) P_(limin) ≤ P_(li) ≤ P_(limax)Q_(cimin) ≤ Q_(ci) ≤ Q_(cimax)

wherein, P_(gi) and Q_(gi) respectively indicate the active and reactivepower of the node i, P_(Li) and Q_(Li) respectively indicate the activeand reactive load power of node i; Q_(ci) indicates the compensationcapacity of the compensation node i; V_(i) and θ_(i) respectivelyindicates the voltage amplitude and phase angle of the node i; the nodeadmittance matrix element is G_(ij)+B_(ij); b_(pi) and b_(qi)respectively indicate the load increase directions; Pg_(imin) andPg_(imax) respectively indicate the upper and lower limits of activetreatment of the generator i; Qg_(imin) and Qg_(imax) respectivelyindicates the upper and lower limits of reactive actions of thegenerator i; V_(imin) and V_(imax) respectively indicates the upper andlower limits of voltage of the node i; P_(limin)and P_(limax) indicatethe upper and lower limits for the branch i to transmit the activepower; and Q_(cimin) and Q_(cimax) respectively indicate the maximumvalue and minimal value of compensation capacity of the compensationnode i.

(2) Fuzzification Treatment of the Target Function

In the multiple objective optimization model established above, thestatic load margin and the investment cost of installing the SVC deviceof the system are contradictory and limit each other. In generalsignificance, the multiple objective function does not have the bestresult, that is, it is impossible to optimize all target functions,instead, and the function has a group of effective results having mutualadvantages and disadvantages according to different objectives andmeeting the limiting conditions.

Each target function has different dimensions, so the target functionsare not comparable with each other, and the method of fuzzification settheory can solve this problem by firstly fuzzifying the target functionby using the membership degree function, converting the target functionwith dimension into the target function without dimension to providecomparability, and providing each target function with differentweights, thus converting the multiple objective problem into the singleobjective problem.

1) The greater the static load margin, the better the voltage stabilityof system, so the target function F₁ belongs to the maximum targetfunction, and the membership degree function μ(F₁) is selected as thelinear monotonic increasing function:

$\begin{matrix}{{\mu \left( F_{1} \right)} = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu} F_{1}} \leq F_{1m\; i\; n}} \\\frac{F_{1} - F_{1m\; i\; n}}{F_{1{ma}\; x} - F_{1m\; i\; n}} & {{{if}\mspace{14mu} F_{1m\; i\; n}} \leq F_{1} \leq F_{1m\; {ax}}} \\1 & {{{if}\mspace{14mu} F_{1}} \geq F_{1m\; {ax}}}\end{matrix} \right.} & (18)\end{matrix}$

wherein, F_(1min) indicates the unacceptable target value; F_(1max)indicates the ideal target value.

2) The less the investment cost, the better the target function F₂, sothe target function F2 belongs to the minimal target function, and themembership degree function μ(F₂) is selected as the linear monotonicdecreasing function:

$\begin{matrix}{{\mu \left( F_{2} \right)} = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu} F_{2}} \leq F_{2m\; {ax}}} \\\frac{F_{2\; {ma}\; x} - F_{2}}{F_{2\; {ma}\; x} - F_{2m\; i\; n}} & {{{if}\mspace{14mu} F_{2m\; i\; n}} \leq F_{2} \leq F_{2m\; {ax}}} \\1 & {{{if}\mspace{14mu} F_{2}} \geq F_{1m\; i\; n}}\end{matrix} \right.} & (19)\end{matrix}$

wherein, F_(2max) indicates the unacceptable target value; F_(2min)indicates the ideal target value, and the diagram of linear monotonicincreasing or decreasing membership function is shown in FIG. 3.

(3) Fuzzy Single Objective Optimization Model

The decider provides different weights to all fuzzy target functions andconverts the multiple objective functions into the fuzzy singleobjective function, and the optimization model of SVC capacityconfiguration can be expressed as:

$\begin{matrix}{{F = {\max \left( {\sum\limits_{i = 1}^{2}{\omega_{i}{\mu \left( F_{i} \right)}}} \right)}};} & (20)\end{matrix}$

The limiting condition is the same as the limiting condition of themultiple objective optimization model established in formula (16) andformula (17).

4. Below will exemplify the specific application and verification of allabove mentioned embodiments to verify the technical correctness andfeasibility of the above mentioned SVC Compensation StrategyOptimization Method;

(1) The Test Network of Technical Verification Implementation

The test network of technical verification is the West Huanghe Riverpower grid in Gansu, and the simplified diagram of system electricwiring is shown in FIG. 5 in section 5. The required informationincludes the network parameter of the whole power grid, the elementparameter and the price of SVC device.

(2) Final compensation strategy is shown in Table 2.

TABLE 2 SVC compensation location and compensation capacity SVCcompensation Compensation node capacity/Mvar Gansu Hongliu 31 143 GansuDunhuang 31 458 Gansu Guazhou 31 138 Gansu Guazhou 11 95 GansuDangjinshan wind 69.2 field 11

(3) Comparison of Risk Measurement

Table 3 lists the change of risk measurement before and after thecompensation. The calculation layer amount is 3, and the top 10 highestrisk values are listed in the comparison.

TABLE 3 Comparison of calculation results of risk measurementCalculation result Calculation result Calculation result on the firstlayer on the second layer on the third layer the top 10 the last 10 thetop 10 the last 10 the top 10 the last 10 highest highest highesthighest highest highest sequence risk sequence risk sequence risksequence risk sequence risk sequence risk values before values aftervalues before values after values before values after compensationcompensation compensation compensation compensation compensation0.660711 0.539422 5.245392 3.268788 6.685667 4.137418 0.514425 0.3794794.348637 3.251822 6.660483 4.135580 0.485469 0.379257 4.331710 3.2339846.638323 4.124500 0.484794 0.378288 4.309629 3.225816 6.405220 4.1224350.484763 0.311640 4.300160 3.225142 6.374375 4.121326 0.484478 0.2953204.294920 3.214415 6.313687 4.120538 0.472842 0.288144 4.285009 2.5978646.301657 4.098649 0.442821 0.287920 4.260807 2.447739 6.286619 4.0904700.441937 0.287785 4.249552 2.423476 6.286023 4.084272 0.423108 0.2869503.722775 2.422895 6.277229 4.076745

(4) Comparison of static load margin is shown in Table 4.

TABLE 4 comparison of static load margin before and after SVCcompensation Before the After the compensation compensation Static loadmargin 0.2027 0.4187 SVC investment 0 2242.66 cost/ten-thousand Yuan

(5) The comparison diagram of load node PV curves is shown in FIG. 6 andFIG. 7 in section 5.

In view of the above, all embodiments in the present invention combinethe risk measurement analysis technology with original static loadmargin analysis method to perform the optimization plan of analyzing thereactive weak point of the whole system in normal state and fault state,thus providing the optimal SVC access point. Therefore, according to therisk measurement analysis technology, the system weak point of thesystem in chain accident state can be obtained, the corresponding weakpoint is accessed with the SVC device to compensate the reactive powerfor the system, enhance the system voltage, and prevent the large-scaleblackout accident of power system and the great economic loss and socialinfluence.

At last, it should be noted that: the foregoing description is only madeto the preferred embodiment of the present invention and does not intendto limit the invention. Although the present invention is described indetail referring to the above mentioned embodiments, those skilled inthe art can also modify the technical solution described in the aboveembodiments, or equivalently replace some technical features. Anymodification, equivalent replacement and improvement within the spiritand principle of the invention should all be included in the scope ofprotection of the invention.

1. A static var compensator (SVC) compensation strategy optimizationmethod comprising: a. calculating weak voltage nodes in a fault statebased on risk measurement; b. calculating weak voltage nodes in a normalstate based on a static stability margin; and c. determining optimal SVCdistribution point and calculating optimal configuration of SVCcapacity.
 2. The SVC compensation strategy optimization method accordingto claim 1, wherein the step a specifically comprises: a1. credibilitymeasurement: measuring the uncertainty of the power grid catastrophicaccident by the credibility measurement and establishing the evaluationmodel of the catastrophic accident according to the reliability theory;a2. global fuzzy safety measurement: the ability of the element to bearthe disturbance varies in certain region [D_(1ow), D_(p)]; when thedisturbance is greater than D_(up), the element is unsafe; when thedisturbance is less than D_(low), the element is normal; when thedisturbance occurs within this region, the element running state isuncertain and can be drawn with the region number; and the region numberis a type of special fuzzy number, and the membership degree functioncan be used to draw the change trend; a3. risk measurement: the riskmeasurement M_(risk) is a comprehensive measurement to M_(cr) andM_(GFS) and is positively related to the M_(cr) and M_(GFS), it can bedrawn by the Larsen operator, and the mathematical expression is:M _(risk) =M _(cr) M _(GFS)  (14) a4. SVC node distribution modelalgorithm based on risk measurement: on the basis of catastrophicaccident risk evaluation method, analyzing the running risk of the powergrid, forecasting the weak branch in accident process, obtaining thesequence of possible catastrophic accidents and the sequence of chainfaults of the power grid, and providing basis for SVC compensationpoint.
 3. The SVC compensation strategy optimization method according toclaim 2, wherein in the step a1, the credibility measurement AKA) ofoccurrence of the catastrophic accident A is: $\begin{matrix}{{{{M_{cr}(A)} = {\frac{1}{2}\left( {{M_{pos}(A)} + {M_{{nec}\;}(A)}} \right)}};}{{wherein}\text{:}}} & (3) \\{{{M_{nec}(A)} = {1 - {M_{pos}\left( \overset{\_}{A} \right)}}};} & (4)\end{matrix}$ in formula (3) and formula (4), A is the complementary setof A; and M_(nec)(A) indicates the impossibility degree of Ā; accordingto formula (3) and formula (4), the value in the credibility measurementvaries within [0,1]; when the value is 1, the accident A is evitable;when the value is 0, the accident A is impossible; and when the value isbetween 0 and 1, the credibility of occurrence of the accident Aincreases with the increase of measurement.
 4. The SVC compensationstrategy optimization method according to claim 2, wherein in the stepa2, the over limit degree of the power system component is used torepresent the chain fault severity, and 5 severity membership degreesδt(t=1, 2, . . . , 5) are used to describe the severity of branchoverload, load miss, bus voltage, generator active and reactive output.5. The SVC compensation strategy optimization method according to claim2, wherein in the step a4, the N−1 accident is considered as the initialaccident, then rank the risk measurements of all accident transmissionstages, and the most dangerous accident in one stage is considered asthe initial accident of the next stage; when the accident causes the nonconvergence of power grid trend or more than 20% of load loss, it is acatastrophic accident; and N is a natural number.
 6. The SVCcompensation strategy optimization method according to claim 1, the stepb specifically comprises: obtaining the load margin of the system ornode by the nonlinear planning method, and in the condition of meetingall limits of system, determining the maximum value of load increase inthe power system, and the mathematical model thereof is:min−λ  (15); the limiting condition (s.t.) of formula (15) is asfollows:${P_{gi} - P_{Li} - {V_{i}{\sum\limits_{j \in i}{V_{j}\left( {{G_{ij}\cos \; \theta_{ij}} + {B_{ij}\sin \; \theta_{ij}}} \right)}}} - {\lambda \; b_{pi}}} = 0$${Q_{gi} + Q_{ci} - Q_{Li} - {V_{i}{\sum\limits_{j \in i}{V_{j}\left( {{G_{ij}\sin \; \theta_{ij}} - {B_{ij}\cos \; \theta_{ij}}} \right)}}} - {\lambda \; b_{qi}}} = 0$Pg_(imin) ≤ Pg_(i) ≤ Pg_(imax)(i = 1, 2, …  , n_(G))Qg_(imin) ≤ Qg_(i) ≤ Qg_(imax)V_(imin) ≤ V_(i) ≤ V_(ima x)(i = 1, 2  …  , n)P_(limin) ≤ P_(li) ≤ P_(limax)(i = 1, 2  …  , n_(l)) in formula (15)and the limiting conditions thereof: n indicates the total number ofnodes; P_(gi) and Q_(gi) respectively indicate the active and reactivepower of the node i, P_(Li) and Q_(Li) respectively indicate the activeand reactive load power of node i; V, and θ_(i) respectively indicatesthe voltage amplitude and phase angle of the node i; the node admittancematrix element is G_(ij)+B_(ij); b_(pi) and b_(qi) respectively indicatethe load increase directions; in formula (15) and the limitingconditions thereof: n_(l) indicates the amount of branches, Pg_(imin)and Pg_(imax) respectively indicate the upper and lower limits of activetreatment of the generator i; Qg_(imin) V_(imax) respectively indicatesthe upper and lower limits of reactive actions of the generator i;V_(imin) and V_(imax) respectively indicates the upper and lower limitsof voltage of the node i; P_(limin) and P_(limax) indicate the upper andlower limits for the branch i to transmit the active power.
 7. The SVCcompensation strategy optimization method according to claim 1, the stepc specifically comprises: c1. the multiple objective SVC capacityconfiguration optimization model; c2. the fuzzification treatment oftarget function by using the method of fuzzy set theory; and c3. thefuzzy single objective optimization model.
 8. The SVC compensationstrategy optimization method according to claim 7, the step c1specifically comprises: in the process of configuring the SVC device tothe power grid, it is required to consider both the increase of thesystem voltage stability and the cost of installing the SVC afterinstalling the SVC , therefore, when establishing the optimizationmodel, the target function should include the change of voltagestability and the fee paid; the target function: considering the targetfunction of the static load margin:F₁=max λ  (16); considering the target function of the investment fee:$\begin{matrix}{{F_{2} = {{\min {\sum\limits_{i \in \Omega}a_{i}}} + {b_{i}y_{i}}}};} & (17)\end{matrix}$ wherein: λ indicates the static load margin of the system;Ω indicates the selected reactive compensation node, y_(i) indicates thecompensation reactive capacity of the compensation node i, and a_(i) andb_(i) respectively indicate the relationship parameters between thecompensation price and the compensation capacity; limiting condition:${P_{gi} - P_{Li} - {V_{i}{\sum\limits_{j \in i}{V_{j}\left( {{G_{ij}\cos \; \theta_{ij}} + {B_{ij}\sin \; \theta_{ij}}} \right)}}} - {\lambda \; b_{pi}}} = 0$${Q_{gi} + Q_{ci} - Q_{Li} - {V_{i}{\sum\limits_{j \in i}{V_{j}\left( {{G_{ij}\sin \; \theta_{ij}} - {B_{ij}\cos \; \theta_{ij}}} \right)}}} - {\lambda \; b_{qi}}} = 0$Pg_(imin) ≤ Pg_(i) ≤ Pg_(imax) Qg_(imin) ≤ Qg_(i) ≤ Qg_(imax)V_(imin) ≤ V_(i) ≤ V_(ima x) P_(limin) ≤ P_(li) ≤ P_(limax)Q_(cimin) ≤ Q_(ci) ≤ Q_(cimax) wherein, P_(gi) and Q_(gi) respectivelyindicate the active and reactive power of the node i, P_(Li) and Q_(Li)respectively indicate the active and reactive load power of node i;Q_(ci) indicates the compensation capacity of the compensation node i;V_(i) and θ_(i) respectively indicates the voltage amplitude and phaseangle of the node i; the node admittance matrix element isG_(ij)+B_(ij); b_(pi) and b_(qi) respectively indicate the load increasedirections; Pg_(imin) and Pg_(imax) respectively indicate the upper andlower limits of active treatment of the generator i; Qg_(imin) andQg_(imax) respectively indicates the upper and lower limits of reactiveactions of the generator ; V_(imin) and V_(imax) respectively indicatesthe upper and lower limits of voltage of the node i; P_(limin) andP_(limax) indicate the upper and lower limits for the branch i totransmit the active power; and Q_(cimin) and Q_(cimax) respectivelyindicate the maximum value and minimal value of compensation capacity ofthe compensation node i.
 9. The SVC compensation strategy optimizationmethod according to claim 7, the step c2 specifically comprises: 1) thegreater the static load margin, the better the voltage stability ofsystem, so the target function F₁ belongs to the maximum targetfunction, and the membership degree function μ(F₁) is selected as thelinear monotonic increasing function: $\begin{matrix}{{\mu \left( F_{1} \right)} = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu} F_{1}} \leq F_{1m\; i\; n}} \\\frac{F_{1} - F_{1m\; i\; n}}{F_{1{ma}\; x} - F_{1m\; i\; n}} & {{{if}\mspace{14mu} F_{1m\; i\; n}} \leq F_{1} \leq F_{1m\; {ax}}} \\1 & {{{if}\mspace{14mu} F_{1}} \geq F_{1m\; {ax}}}\end{matrix} \right.} & (18)\end{matrix}$ wherein, F_(1min) indicates the unacceptable target value;F_(1max) indicates the ideal target value; 2) the less the investmentcost, the better the target function F₂, so the target function F₂belongs to the minimal target function, and the membership degreefunction μ(F₂) is selected as the linear monotonic decreasing function:$\begin{matrix}{{\mu \left( F_{2} \right)} = \left\{ {\begin{matrix}0 & {{{if}\mspace{14mu} F_{2}} \leq F_{2m\; {ax}}} \\\frac{F_{2\; {ma}\; x} - F_{2}}{F_{2\; {ma}\; x} - F_{2m\; i\; n}} & {{{if}\mspace{14mu} F_{2m\; i\; n}} \leq F_{2} \leq F_{2m\; {ax}}} \\1 & {{{if}\mspace{14mu} F_{2}} \geq F_{2m\; i\; n}}\end{matrix};} \right.} & (19)\end{matrix}$ wherein, F_(2max) indicates the unacceptable target value;F_(2min) indicates the ideal target value, and the diagram of linearmonotonic increasing or decreasing membership function.
 10. The SVCcompensation strategy optimization method according to claim 7, the stepc3 specifically comprises: the decider applies different weights to allfuzzy target functions and converts the multiple objective functionsinto the fuzzy single objective function, and the optimization model ofSVC capacity configuration can be expressed as: $\begin{matrix}{{F = {\max \left( {\sum\limits_{i = 1}^{2}{\omega_{i}{\mu \left( F_{i} \right)}}} \right)}};} & (20)\end{matrix}$ the limiting condition is the same as the limitingcondition of the multiple objective optimization model established informula (16) and formula (17).